Machine Learning
Machine learning (ML) focuses on developing algorithms that allow computers to learn from data without explicit programming, aiming to improve prediction accuracy, automate tasks, and extract insights. Current research emphasizes areas like fairness in federated learning, efficient model training and deployment (including techniques to reduce communication overhead), and enhancing model interpretability and robustness against adversarial attacks. ML's impact spans diverse fields, from healthcare (e.g., disease prediction) and industrial quality control to astrophysics (e.g., galaxy classification) and cybersecurity, demonstrating its broad applicability and significant potential for scientific advancement and practical problem-solving.
Papers
The Significance of Machine Learning in Clinical Disease Diagnosis: A Review
S M Atikur Rahman, Sifat Ibtisum, Ehsan Bazgir, Tumpa Barai
Accurate Crop Spraying with RTK and Machine Learning on an Autonomous Field Robot
W. M. T. D. Wijesundara, T. D. Wanigathunga, M. N. C. Waas, R. T. Hithanadura, S. R. Munasinghe
Parcel loss prediction in last-mile delivery: deep and non-deep approaches with insights from Explainable AI
Jan de Leeuw, Zaharah Bukhsh, Yingqian Zhang
An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing XGBoost and xDeepFM Algorithms
Weinan Dai, Yifeng Jiang, Chengjie Mou, Chongyu Zhang
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs
Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang, Yonghao Zhuang, Jindong Chen, Eric Xing, Zhiting Hu
Performance Tuning for GPU-Embedded Systems: Machine-Learning-based and Analytical Model-driven Tuning Methodologies
Adrian Perez Dieguez, Margarita Amor Lopez
MLFMF: Data Sets for Machine Learning for Mathematical Formalization
Andrej Bauer, Matej Petković, Ljupčo Todorovski
Design Of Rubble Analyzer Probe Using ML For Earthquake
Abhishek Sebastian, R Pragna, K Vishal Vythianathan, Dasaraju Sohan Sai, U Shiva Sri Hari Al, R Anirudh, Apurv Choudhary
Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization
Mohammad Mohammadi, Ali Mohammadi
Evaluating machine learning models in non-standard settings: An overview and new findings
Roman Hornung, Malte Nalenz, Lennart Schneider, Andreas Bender, Ludwig Bothmann, Bernd Bischl, Thomas Augustin, Anne-Laure Boulesteix
On the Detection of Image-Scaling Attacks in Machine Learning
Erwin Quiring, Andreas Müller, Konrad Rieck
Machine Learning and Knowledge: Why Robustness Matters
Jonathan Vandenburgh
Meta learning with language models: Challenges and opportunities in the classification of imbalanced text
Apostol Vassilev, Honglan Jin, Munawar Hasan
Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey
Zhibo Xing, Zijian Zhang, Jiamou Liu, Ziang Zhang, Meng Li, Liehuang Zhu, Giovanni Russello
BatteryML:An Open-source platform for Machine Learning on Battery Degradation
Han Zhang, Xiaofan Gui, Shun Zheng, Ziheng Lu, Yuqi Li, Jiang Bian
Random Forest Kernel for High-Dimension Low Sample Size Classification
Lucca Portes Cavalheiro, Simon Bernard, Jean Paul Barddal, Laurent Heutte
Federated learning compression designed for lightweight communications
Lucas Grativol Ribeiro, Mathieu Leonardon, Guillaume Muller, Virginie Fresse, Matthieu Arzel